Case Study: Audioburst achieves scalable, real-time spoken-audio search with Microsoft Azure

Audio AI experts use cloud-based search platform to connect listeners to audio content

Audioburst is an AI-driven audio search startup that aims to organize the world’s spoken-word content so people can find clips from radio, podcasts, and broadcasts. Faced with the challenge of indexing millions of minutes of live and prerecorded audio daily—and doing so with a lean team and startup resources—the company needed a scalable way to turn speech into searchable, actionable content for voice assistants, mobile apps, and in-car systems.

Audioburst built its pipeline on Microsoft Azure, using Azure Search, Cognitive Services (LUIS), Media Services, Service Fabric, GPU servers, and Azure Bot Service to convert audio into short “bursts,” extract metadata, detect user intent, and serve relevant clips and recommendations. The solution scales efficiently—processing over a million minutes of live audio per month on a single server—improves response times for voice queries, and benefits from ongoing technical support and integration with voice platforms.

Audioburst

Gal Klein

Cofounder and Chief Technical Officer